摘要
为了检测复杂环境下枸杞的虫害情况,提出基于改进YOLOv5m的模型.以下一代视觉转换器(Next-ViT)作为骨干网络,提高模型的特征提取能力,使模型更加关注关键目标特征.在模型颈部增加自适应融合的上下文增强模块,增强模型对上下文信息的理解与处理能力,提高模型对小目标(蚜虫)的检测精度.将颈部网络中的C3模块替换为C3_Faster模块,减少模型占用量并进一步提高模型检测精度.实验结果表明,所提模型的准确率和召回率分别为97.0%、92.1%,平均精度均值为94.7%;相比于YOLOv5m,所提模型的平均精度均值提高了1.9个百分点,蚜虫的检测平均精度提高了9.4个百分点.对比不同模型的平均精度均值,所提模型比主流模型YOLOv7、YOLOX、DETR、EfficientDet-D1、Cascade R-CNN分别高1.6、1.6、2.8、3.5、1.0个百分点.所提模型在提高检测性能的同时,模型占用量也保持在合理范围内.
A model based on improved YOLOv5m was proposed for wolfberry pest detection in a complex environment.The next generation vision transformer(Next-ViT)was used as the backbone network to improve the feature extraction ability of the model,and the key target features were given more attention by the model.An adaptive fusion context enhancement module was added to the neck to enhance the model’s ability to understand and process contextual information,and the precision of the model for the small object(aphids)detection was improved.The C3 module in the neck network was replaced by using the C3_Faster module to reduce the model footprint and further improve the model precision.Experimental results showed that the proposed model achieved a precision of 97.0%and a recall of 92.1%.The mean average precision(mAP50)was 94.7%,which was 1.9 percentage points higher than that of the YOLOv5m,and the average precision of aphid detection was improved by 9.4 percentage points.The mAP50 of different models were compared and the proposed was 1.6,1.6,2.8,3.5,and 1.0 percentage points higher than the mainstream models YOLOv7,YOLOX,DETR,EfficientDet-D1,and Cascade R-CNN,respectively.The proposed model improves the detection performance while maintaining a reasonable model footprint.
作者
杜丁健
高遵海
陈倬
DU Dingjian;GAO Zunhai;CHEN Zhuo(School of Mathematics and Computer Science,Wuhan Polytechnic University,Wuhan 430048,China;School of Management,Wuhan Polytechnic University,Wuhan 430048,China)
出处
《浙江大学学报（工学版）》
EI
CAS
CSCD
北大核心
2024年第10期1992-2000,共9页
Journal of Zhejiang University：Engineering Science
基金
湖北省社会科学基金资助项目(21ZD072).
